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International Refereed Journal of Engineering and Science (IRJES)

International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications

International Refereed Journal of Engineering and Science (IRJES)

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International Refereed Journal of Engineering and Science (IRJES)ISSN (Online) 2319-183X, (Print) 2319-1821Volume 2, Issue 3(March 2013), PP.01-06www.irjes.comwww.irjes.com 1 | PageImproving High Quality Privacy Preserving Location MonitoringSystem For Wireless Sensor Networks1 . Ms. Badi Alekhya.,1Assistant Professor, T.J.S Engineering College, Department of Computer Science and EngineeringAbstract: - . Message authentication is an important objective of information security in modern wireless net-works. This objective is met by providing the receiver of the message an assurance of the sender’s identity. Digi-tal tools have been developed using cryptography. A major limitations of all cryptographic methods for messageauthentication lies in their uses of algorithms with fixed symmetric or public keys.This project presents a syn-chronized random key generation for each node which depends on the position. So there is no need any exactlocation data to transfer. Key generation protocol is used here. Key generation protocol depends on the globalPositioning method. Pseudo random number creation depends upon the position and the initial value of linearfeedback shift register. So each and every second create a new key. So the level of privacy information securityis too high and power consumption is very low.1. INTRODUCTIONTHE advance in wireless sensor technologies has resulted in many new applications for military and orcivilian purposes. Many cases of these applications rely on the information of personal locations, for example,surveillance and location systems. These location-dependent systems are realized by using either identity sen-sors or counting sensors. For identity sensors, for example, Bat and Cricket, each individual has to carry a signalsender/receiver unit with a globally unique identifier. With identity sensors, the system can pinpoint the exactlocation of each monitored person. On the other hand, counting sensors, for example, photoelectric sensors, [4],and thermal sensors [5], are deployed to report the number of persons located in their sensing areas to a server.Fig. 1 gives an example of a privacy breach in a location of the monitoring system with counting sensors. Thereare the11 counting sensor nodes installed in nine rooms R1 to R9, and two hallways C1 and C2 (Fig. 1a). Thenonzero number of persons detected by each sensor node is depicted as a number in parentheses. Figs. 1b and 1cgive the numbers reported by the same set of sensor nodes at two consecutive time insistanstancesti+1 and ti+2,respectively. If R3 is Alice’s office room, an adversary knows that Alice is in room R3 at time ti. Then, the ad-versary knows that Alice left R3 at time ti+1 and went to C2by knowing the number of persons detected by thesensor nodes in R3 and C2. Likewise, the adversary can infer that Alice left C2 at time ti+2 and went to R7.Such knowledge leakage may lead to several privacy threats. For example, knowing that a person has visitedcertain clinical rooms may lead to knowing her health records. Also, knowing that a person has visited a certainbar or restaurant in a mall building may reveal confidential personal information.Fig1. A location monitoring system using counting sensors. (a) At time ti. (b) At time ti+1. (c) At time ti+2.2. RELATED WORKStraightforward approaches for preserving users’ location privacy include enforcing privacy policies torestrict the use of collected location information and anonymizing the stored data before any disclosure. How-

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Improving High Quality Privacy Preserving Location Monitoring System For Wireless Sensorwww.irjes.com 2 | Pageever, these approaches fail to prevent internal data thefts or inadvertent disclosure. Recently, location anonymi-zation techniques have been widely used to anonymize personal location infomation before any server to gathersthe location information, in order to preserve personal location privacy in location-based services. These tech-niques are based on one of the three concepts.1)False locations.Instead of reporting the monitored object’s exact location, the object reports n different locations,where only one of them is the object’s actual location while the rest are false locations2) Spatial cloaking.The spatial cloaking technique blurs a user’s location into a cloaked spatial area that satisfy the user’sspecified privacy requirements3) Space transformationThis technique transforms the location information of queries and data into another space, where thespatial relationship among the query and data are encoded .Among these three privacy concepts, only the spatialcloaking technique can be applied to our problem. The main reasons for this are that 1) the false location tech-niques cannot provide high-quality monitoring services due to a large amount of false location information, 2)the space transformation techniques cannot provide privacy preserving monitoring services as it reveals themonitored object’s exact location information to the query issuer.The spatial cloaking techniques can provide aggregate location information to the server and balance atrade off between privacy protection and the quality of services by tuning the specified privacy requirements, forexample, k anonymity and minimum area privacy requirements. Thus, we adopt the spatial cloaking techniqueto preserve the monitored object’s location privacy in our location monitoring system.3.ALGORITHMSLOCATION ANONYMIZATION ALGORITHMSIn this section, we present our in-network resource and quality-aware location anonymization algo-rithms, that is.periodically executed by the sensor nodes to report their k-anonymous aggregate locations to theserver for every reporting period.3.1 The Resource-Aware AlgorithmAlgorithm 1.Resource-aware location anonymization1: function RESOURCEAWARE (Integer k, Sensor m,List R)2: PeerList <-{0}// Step 1: The broadcast step3: Send a message with m’s identity m.ID, sensing aream.Area, and object count m.Count to m’s neighbour peers4: if Receive a message from a peer p, i.e., (p.ID, p.Area, p.count) then5: Add the message to PeerList6: if m has found an adequate number of objects then7: Send a notification message to m’s neighbours8: end if9: if Some m’s neighbour has not found an adequate number of objects then10: Forward the message to m’s neighbours11: end if12: end if// Step 2: The cloaked area step13: S<-{m}14: Compute a score for each peer in PeerList15: Repeatedly select the peer with the highest score fromPeerList to S until the total number of objects in S is at least k16: Area a minimum bounding rectangle of the senornodes in S17: N the total number of objects in S// Step 3: The validation step18: if No containment relationship with Area and R 2 R then

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Improving High Quality Privacy Preserving Location Monitoring System For Wireless Sensorwww.irjes.com 3 | Page19: Send (Area,N) to the peers within Area and the server20: else if m’s sensing area is contained by some R 2 R then21: Randomly select a R0 2 R such that R0:Area contains m’s sensing area22: Send R0 to the peers within R0:Area and the server23: else24: Send Area with a cloaked N to the peers within Area and the server25: end if3.2 Quality-aware location anonymizationalgorithm1: function QUALITYAWARE (Integer k, Sensor m,Set init_solution, List R)2: current_min_cloaked_area init_solution// Step 1: The search space step3: Determine a search space S based on init_solution4: Collect the information of the peers located in S// Step 2: The minimal cloaked area step5: Add each peer located in S to C½1_ as an item6: Add m to each item set in C½1_ as the first item7: for i ¼ 1; i _ 4; i ++ do8: for each item set X ¼ fa1; . . . ; aiþ1g in C½i_ do9: if Area(MBR(X))< Area(current_min_cloaked_area) then10: if N(MBR(X)) >=k then11: current_min_cloaked_area<-{x}12: Remove X from C½i_13: end if14: else15: Remove X from C½i_16: end if17: end for18: if i < 4 then19: for each item set pair X ¼ fx1; . . . ; xiþ1g,Y ¼ fy1; . . . ; yiþ1g in C½i_ do20: if x1 ¼ y1; . . . ; xi ¼ yi and xiþ1 6¼ yiþ1 then21: Add an item set fx1; . . . ; xiþ1; yiþ1gto C½i þ 1_22: end if23: end for24: end if25: end for26: Area a minimum bounding rectangle ofcurrent_min_cloaked_area27: N the total number of objects incurrent_min_cloaked_area// Step 3: The validation step28: Lines 18 to 25 in Algorithm 1

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Improving High Quality Privacy Preserving Location Monitoring System For Wireless Sensorwww.irjes.com 4 | Page4. System Architecturefig2.System ArchitectureSensor nodes:Each sensor node is responsible for determining the number of objects in its sensing area,blurring itssensing area into a cloaked area A, which includes at least k objects, and reporting A with the number of objectslocated in A as aggregate location information to the server. We do not have any assumption about then networktopology, as our system only requires a communication path from each sensor node to the server through a dis-tributed tree [10]. Each sensor node is also aware of its location and sensing area.Server:The server is responsible for collecting the aggregate locations reported from the sensor nodes, using aspatial histogram to estimate the distribution of the monitored objects, and answering range queries based on theestimated object distribution. Furthermore, the administrator can change the anonymized level k of the system atanytime by disseminating a message with a new value of k to all the sensor nodes.System users:Authenticated administrators and users can issue range queries to our system through either the serveror the sensor nodes, as depicted in Fig. 2. The server uses the spatial histogram to answer their queries.Privacy model:In our system, the sensor nodes constitute a trusted zone, where they behave as defined in our algo-rithm and communicate with each other through a secure network channel to avoid internal network attacks, forexample, eavesdropping, traffic analysis, and malicious nodes. Since establishing such a secure network channelhas been studied in the literature the discussion of how to get this network channel is beyond the scope of thispaper. However, the solutions that have been used in previous works can be applied to our system. Our systemalso provides anonymous communication between the sensor nodes and the server by employing existinganonymous communication techniques. Thus given an aggregate location R, the server only knows that thesender of R is one of the sensor nodes within R.Furthermore,only authenticated administrators can change the k-anonymity level and the spatial histogram size. In emergency cases, the administrators can set the k-anonymitylevel to a small value to get more accurate aggregate locations from the sensor nodes, or even set it to zero todisable our algorithm to get the original readings from the sensor nodes, in order to get the best services fromthe system. Since the server and the system user are outside the trusted zone, they are untrusted.We now discussthe privacy threat in existing location monitoring systems. In an identity-sensor location monitoring system,since each sensor node reports the exact location information of each monitored object to the server, the adver-sary can pinpoint each object’s exact location. On the other hand, in a counting-sensor location monitoring sys-tem, each sensor node reports the number of objects in its sensing area to the server. The adversary can map themonitored areas of the sensor nodes to the system layout. If the object count of a monitored area is very small orequal to one, the adversary can infer the identity of the monitored objects based on the mapped monitored area,Performance MetricsWe evaluate our system in terms of five performance metrics.1. Attack model error. This metric measures the resilience of our system to the attacker model by the relativeerror between the estimated number of objects b N in a sensor node’s sensing area and the actual one N. Theerror is measured as |N-N|/N. When N=0, we consider N^ as the error.2. Communication cost. We measure the communication cost of our location anonymization algorithms interms of the average number of bytes sent by each sensor node per reporting period. This metric also indicatesthe network traffic and the power consumption of the sensor nodes.3. Cloaked area size. This metric measures the quality of the aggregate locations reported by the sensornodes.The smaller the cloaked area,the better the accuracy of the aggregate location.

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Improving High Quality Privacy Preserving Location Monitoring System For Wireless Sensorwww.irjes.com 5 | Page4. Computational cost. We measure the computational cost of our location anonymization al gorithms in termsof the average number of the MBR computations that are needed to determine a resource or quality-awarecloaked area. We compare our algorithms with a basic approach that computes the MBR for each combinationof the peers in the required search space to find the minimal cloaked area. The basic approach does not employany optimization techniques proposed for our quality-aware algorithm.5. Query error. This metric measures the utility of our system, in terms of the relative error between the queryof the answer M^, which is the estimated number of objects within the query region based on a spatial histo-gram, and the actual answer M,respectively.The error is measured as |M^-M|/M When M = 0, we consider M^as the error.Fig Attacker model error. (a) Anonymity levels. (b) Number of objectsEffect of the Number of ObjectsFig. depicts the performance of our system with respect to the increasing the number of objects from 2,000 to10,000. Fig. shows that when the number of objects increases, the communication cost of the resource-awarealgorithm is only slightly affected, but the quality-aware algorithm significantly reduces the communicationcost. The broadcast step of the resource-aware algorithm effectively allows each sensor node to find an adequatenumber of objects to blur its sensing area. When there are more objects, the sensor node finds smaller cloakedareas that satisfy the k-anonymity privacy requirement, as given in Fig. b. Thus, the required search space of aminimal cloaked area computed by the quality aware algorithm becomes smaller; hence, the communicationcost of gathering the information of the peers in such a smaller required search space reduces. Likewise, sincethere are fewer peers in the smaller required search space as the number of objects increases, finding the mini-mal cloaked area incurs less MBR computation (Fig. c). Since our algorithms generate smaller cloaked areaswhen there are more users, the spatial histogram can gather more accurate aggregate locations to estimate theobject distribution; therefore, the query answer error reduces (Fig. d). The result also shows that the quality-aware algorithm always provides better quality services than the resource-aware algorithm.Fi Anonymity levels. (a) Communications cost. (b) Cloaked area size. (c) Computational cost. (d) Estima-tion errorCONCLUSIONIn this paper, we propose a privacy-preserving location monitoring system for wireless sensor net-works. We design two in-network location anonymization algorithms, namely, resource and quality-aware algo-rithms, which preserve personal location privacy, while enabling the system to provide allocation monitoringservices. Both algorithms rely on this of well-established k-anonymity privacy concept that requires a person isindistinguishable among k persons. In our system, sensor nodes execute our location anonymization algorithmsto provide k-anonymous aggregate locations, in which each aggregate location is a cloaked area A with thenumber of monitored objects, N, located in A, where N>= k, for the system. The resource-aware algorithm aimsto minimize communication and computational cost, while the quality-aware algorithm aims to minimize thesize of cloaked areas in order to generate more accurate aggregate locations. To provide location monitoringservices based on the aggregate location information, we propose a spatial histogram approach that analyzes theaggregate locations reported from the sensor nodes to estimate the distribution of the monitored objects. Theestimated distribution is used to provide location monitoring services through answering range queries. Weevaluate our system through simulated experiments. The results show that our system provides high-quality lo-